Modeling and simulation of multi-UAV, multi-operator surveillance systems

Humann, James; Spero, Eric

doi:10.1109/syscon.2018.8369546

Cited by 15 publications

(11 citation statements)

References 32 publications

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“…The specific type of UAVs modeled and implemented in this research were quadcopters. These vehicles are relatively easy to model compared to RC planes and have different maneuvering capabilities [5]. Quadcopters (rotorcrafts) have gained interest in the drone industry due to their size and unique capabilities such as hovering.…”

Section: Experimentation Setup and Resultsmentioning

confidence: 99%

“…Unmanned Aerial Vehicle (UAVs) are deployed as a group for a variety of application domains such as military reconnaissance and surveillance, search and rescue, science data collection, agriculture, payload delivery, or as flying ad-hoc networks (FANET) to support wireless communications [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…In the decentralized control typology, the system aims to defer human intervention as much as possible and operate autonomously [5,18]. Furthermore, in this typology, UAVs can have leader-subordinate configuration [34] where one UAV is the designated commander of the group and it makes high level decisions for the multi-UAV system.…”

Section: Introductionmentioning

confidence: 99%

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Model-Based Approach to Engineering Resilience in Multi-UAV Systems

Ordoukhanian

Madni

2019

Systems

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Multi-UAV Operations are an area of great interest in government, industry, and research community. In multi-UAV operations, a group of unmanned aerial vehicles (UAVs) are deployed to carry out missions such as search and rescue or disaster relief. As multi-UAV systems operate in an open operational environment, many disrupting events can occur. To this end, resilience of these systems is of great importance. The research performed and reported in this paper utilizes simulation-based research methodology and demonstrates that resilience of multi-UAV systems can be achieved by real-time evaluation of resilience alternatives during system operation. This evaluation is done using a dynamic utility function where priorities change as a function of context. Simulation results show that resilience response can in fact change depending on the context.

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Section: Experimentation Setup and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model-Based Approach to Engineering Resilience in Multi-UAV Systems

Ordoukhanian

Madni

2019

Systems

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“…For instance, Humann et. al [18] builds a model of a multi-UAV, multi-operator surveillance system, simulating the impact of the number of human operators and two types of UAVs on team performance. However, it fails to model individual differences in humans adequately and, more importantly, does not provide any methods to optimize the initial workload distribution.…”

Section: Background and Preliminarymentioning

confidence: 99%

“…The difficulty level can be weighted by the individual operational skill level of the operator. The probability of correct image classification by the operator is nonlinearly influenced by these factors [18] as:…”

Section: B Simulation Environmentmentioning

confidence: 99%

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Wang¹,

Zhao²,

Min³

2023

Preprint

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Multi-human multi-robot teams have great potential for complex and large-scale tasks through the collaboration of humans and robots with diverse capabilities and expertise.To efficiently operate such highly heterogeneous teams and maximize team performance timely, sophisticated initial task allocation strategies that consider individual differences across team members and tasks are required. While existing works have shown promising results in reallocating tasks based on agent state and performance, the neglect of the inherent heterogeneity of the team hinders their effectiveness in realistic scenarios. In this paper, we present a novel formulation of the initial task allocation problem in multi-human multirobot teams as contextual multi-attribute decision-make process and propose an attention-based deep reinforcement learning approach. We introduce a cross-attribute attention module to encode the latent and complex dependencies of multiple attributes in the state representation. We conduct a case study in a massive threat surveillance scenario and demonstrate the strengths of our model.

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Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance

Humann

Fletcher

Gerdes

2023

Systems Engineering

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As unmanned vehicles become smaller and more autonomous, it is becoming feasible to use them in large groups with comparatively few human operators. Design and analysis of such distributed systems are complicated by the many interactions among agents and phenomena of human behavior. In particular, human susceptibility to fatigue and cognitive overload can introduce errors and uncertainty into the system. In this paper, we demonstrate how advanced computational tools can help to overcome these engineering difficulties by optimizing multirobot, multioperator surveillance systems for cost, speed, accuracy, and stealth according to diverse user preferences in multiple case studies. The tool developed is a graphical user interface that returns the optimal number and mix of diverse agent types as a function of the user's trade‐off preferences. System performance prediction relies on a multiagent simulation with submodels for human operators, fixed‐wing unmanned aerial vehicles (UAVs), quadrotor UAVs, and flapping wing UAVs combined in different numbers.

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Modeling and simulation of multi-UAV, multi-operator surveillance systems

Cited by 15 publications

References 32 publications

Model-Based Approach to Engineering Resilience in Multi-UAV Systems

Model-Based Approach to Engineering Resilience in Multi-UAV Systems

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance

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